Personalized news article recommendation provides interesting articles to a specific user based on the preference of the user. With increasing use of hand-held devices, the interests of users are not influenced only by news contents, but also by their location. Therefore, in this paper, we propose a novel topic model to incorporate user location into a user preference for the location-based per...

This article investigates how consistent different newspapers are in their choice of words when writing about the same news events. News articles on the same news events were taken from three Finnish newspapers and compared in regard to their central concepts and words representing the concepts in the news texts. Consistency figures were calculated for each set of three articles (the total numb...

We propose an indicator to measure the degree to which a particular news article is novel, as well as an indicator to measure the degree to which a particular news item attracts attention from investors. The novelty measure is obtained by comparing the extent to which a particular news article is similar to earlier news articles, and an article is regarded as novel if there was no similar artic...

The proliferation of social media is undoubtedly changing the way people produce and consume news online. Editors and publishers in newsrooms need to understand user engagement and audience sentiment evolution on various news topics. News consumers want to explore public reaction on articles relevant to a topic and refine their exploration via related entities, topics, articles and tweets. I wi...

Social news aggregator services generate readers’ subjective reactions to news opinion articles. Can we use those as a resource to classify articles as liberal or conservative, even without knowing the self-identified political leaning of most users? We applied three semi-supervised learning methods that propagate classifications of political news articles and users as conservative or liberal, ...

In order to construct a news database with a function of video on demand (VOD), it is required to classify news articles into topics. In this paper, we propose a method to automatically index and classify TV news articles into 10 topics based on a speech dictation techniques using speaker independent triphone HMMs and word bigram.

A typical news web page consists of news articles. Along with the news article content tags, it also contains tags of navigation links, privacy & copyright information and advertisements. These tags are called as noise tags. Given an online news article in html form, existing works extract articles by discovering informative tags using various heuristic techniques. In this paper, we follow an a...

Paraphrases play an important role in the variety and complexity of natural language documents. However they adds to the difficulty of natural language processing. Here we describe a procedure for obtaining paraphrases from news article. A set of paraphrases can be useful for various kinds of applications. Articles derived from different newspapers can contain paraphrases if they report the sam...

Subgroup discovery aims at constructing symbolic rules that describe statistically interesting subsets of instances with a chosen property of interest. Semantic subgroup discovery extends standard subgroup discovery approaches by exploiting ontological concepts in rule construction. Compared to previously developed semantic data mining systems SDM-SEGS and SDM-Aleph, this paper presents a gener...

In this paper we focus on event extraction from Tamil news article. This system utilizes a scoring scheme for extracting and grouping event-specific sentences. Using this scoring scheme eventspecific clustering is performed for multiple documents. Events are extracted from each document using a scoring scheme based on feature score and condition score. Similarly event specific sentences are clu...